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152 lines
6.1 KiB
Python
152 lines
6.1 KiB
Python
#==========================================================#
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# Shoreline extraction from satellite images
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#==========================================================#
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# Kilian Vos WRL 2018
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#%% 1. Initial settings
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# load modules
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import os
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import numpy as np
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import pickle
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import warnings
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warnings.filterwarnings("ignore")
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import matplotlib.pyplot as plt
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from coastsat import SDS_download, SDS_preprocess, SDS_shoreline, SDS_tools, SDS_transects
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# region of interest (longitude, latitude in WGS84)
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polygon = [[[151.301454, -33.700754],
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[151.311453, -33.702075],
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[151.307237, -33.739761],
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[151.294220, -33.736329],
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[151.301454, -33.700754]]]
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# can also be loaded from a .kml polygon
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#kml_polygon = os.path.join(os.getcwd(), 'examples', 'NARRA_polygon.kml')
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#polygon = SDS_tools.polygon_from_kml(kml_polygon)
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# date range
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dates = ['2017-12-01', '2018-01-01']
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# satellite missions
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sat_list = ['S2']
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# name of the site
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sitename = 'NARRA'
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# filepath where data will be stored
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filepath_data = os.path.join(os.getcwd(), 'data')
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# put all the inputs into a dictionnary
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inputs = {
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'polygon': polygon,
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'dates': dates,
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'sat_list': sat_list,
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'sitename': sitename,
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'filepath': filepath_data
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}
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#%% 2. Retrieve images
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# retrieve satellite images from GEE
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metadata = SDS_download.retrieve_images(inputs)
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# if you have already downloaded the images, just load the metadata file
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metadata = SDS_download.get_metadata(inputs)
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#%% 3. Batch shoreline detection
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# settings for the shoreline extraction
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settings = {
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# general parameters:
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'cloud_thresh': 0.5, # threshold on maximum cloud cover
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'output_epsg': 28356, # epsg code of spatial reference system desired for the output
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# quality control:
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'check_detection': True, # if True, shows each shoreline detection to the user for validation
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'save_figure': True, # if True, saves a figure showing the mapped shoreline for each image
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# add the inputs defined previously
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'inputs': inputs,
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# [ONLY FOR ADVANCED USERS] shoreline detection parameters:
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'min_beach_area': 4500, # minimum area (in metres^2) for an object to be labelled as a beach
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'buffer_size': 150, # radius (in metres) of the buffer around sandy pixels considered in the shoreline detection
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'min_length_sl': 200, # minimum length (in metres) of shoreline perimeter to be valid
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'cloud_mask_issue': False, # switch this parameter to True if sand pixels are masked (in black) on many images
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'dark_sand': False, # only switch to True if your site has dark sand (e.g. black sand beach)
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}
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# [OPTIONAL] preprocess images (cloud masking, pansharpening/down-sampling)
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SDS_preprocess.save_jpg(metadata, settings)
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# [OPTIONAL] create a reference shoreline (helps to identify outliers and false detections)
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settings['reference_shoreline'] = SDS_preprocess.get_reference_sl(metadata, settings)
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# set the max distance (in meters) allowed from the reference shoreline for a detected shoreline to be valid
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settings['max_dist_ref'] = 100
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# extract shorelines from all images (also saves output.pkl and shorelines.kml)
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output = SDS_shoreline.extract_shorelines(metadata, settings)
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# plot the mapped shorelines
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fig = plt.figure()
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plt.axis('equal')
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plt.xlabel('Eastings')
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plt.ylabel('Northings')
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plt.grid(linestyle=':', color='0.5')
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for i in range(len(output['shorelines'])):
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sl = output['shorelines'][i]
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date = output['dates'][i]
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plt.plot(sl[:,0], sl[:,1], '.', label=date.strftime('%d-%m-%Y'))
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plt.legend()
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mng = plt.get_current_fig_manager()
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mng.window.showMaximized()
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fig.set_size_inches([15.76, 8.52])
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#%% 4. Shoreline analysis
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# if you have already mapped the shorelines, load the output.pkl file
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filepath = os.path.join(inputs['filepath'], sitename)
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with open(os.path.join(filepath, sitename + '_output' + '.pkl'), 'rb') as f:
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output = pickle.load(f)
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# now we have to define cross-shore transects over which to quantify the shoreline changes
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# each transect is defined by two points, its origin and a second point that defines its orientation
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# there are 3 options to create the transects:
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# - option 1: draw the shore-normal transects along the beach
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# - option 2: load the transect coordinates from a .kml file
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# - option 3: create the transects manually by providing the coordinates
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# option 1: draw origin of transect first and then a second point to define the orientation
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transects = SDS_transects.draw_transects(output, settings)
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# option 2: load the transects from a .geojson file
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#geojson_file = os.path.join(os.getcwd(), 'examples', 'NARRA_transects.geojson')
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#transects = SDS_tools.transects_from_geojson(geojson_file)
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# option 3: create the transects by manually providing the coordinates of two points
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#transects = dict([])
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#transects['Transect 1'] = np.array([[342836, 6269215], [343315, 6269071]])
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#transects['Transect 2'] = np.array([[342482, 6268466], [342958, 6268310]])
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#transects['Transect 3'] = np.array([[342185, 6267650], [342685, 6267641]])
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# intersect the transects with the 2D shorelines to obtain time-series of cross-shore distance
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settings['along_dist'] = 25
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cross_distance = SDS_transects.compute_intersection(output, transects, settings)
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# plot the time-series
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from matplotlib import gridspec
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fig = plt.figure()
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gs = gridspec.GridSpec(len(cross_distance),1)
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gs.update(left=0.05, right=0.95, bottom=0.05, top=0.95, hspace=0.05)
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for i,key in enumerate(cross_distance.keys()):
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if np.all(np.isnan(cross_distance[key])):
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continue
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ax = fig.add_subplot(gs[i,0])
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ax.grid(linestyle=':', color='0.5')
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ax.set_ylim([-50,50])
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ax.plot(output['dates'], cross_distance[key]- np.nanmedian(cross_distance[key]), '-^', markersize=6)
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ax.set_ylabel('distance [m]', fontsize=12)
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ax.text(0.5,0.95,'Transect ' + key, bbox=dict(boxstyle="square", ec='k',fc='w'), ha='center',
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va='top', transform=ax.transAxes, fontsize=14)
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mng = plt.get_current_fig_manager()
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mng.window.showMaximized()
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fig.set_size_inches([15.76, 8.52]) |